{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import TOSICA\n",
    "import scanpy as sc\n",
    "import numpy as np\n",
    "import warnings \n",
    "warnings.filterwarnings (\"ignore\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Information of GPU"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.7.1\n",
      "(8, 6) NVIDIA GeForce RTX 3080 Ti Laptop GPU\n"
     ]
    }
   ],
   "source": [
    "import torch\n",
    "print(torch.__version__)\n",
    "print(torch.cuda.get_device_capability(device=None),  torch.cuda.get_device_name(device=None))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Reference data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "View of AnnData object with n_obs × n_vars = 10600 × 3000\n",
      "    obs: 'Celltype'\n",
      "    var: 'Gene Symbol'\n",
      "alpha          3136\n",
      "beta           2966\n",
      "ductal         1290\n",
      "acinar         1144\n",
      "delta           793\n",
      "PSC             524\n",
      "PP              356\n",
      "endothelial     273\n",
      "macrophage       52\n",
      "mast             25\n",
      "epsilon          21\n",
      "schwann          13\n",
      "t_cell            7\n",
      "Name: Celltype, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "ref_adata = sc.read('demo_train.h5ad')\n",
    "ref_adata = ref_adata[:,ref_adata.var_names]\n",
    "print(ref_adata)\n",
    "print(ref_adata.obs.Celltype.value_counts())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Query data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "View of AnnData object with n_obs × n_vars = 4218 × 3000\n",
      "    obs: 'Celltype'\n",
      "    var: 'Gene Symbol'\n",
      "alpha           2011\n",
      "beta            1006\n",
      "ductal           414\n",
      "PP               282\n",
      "acinar           209\n",
      "delta            188\n",
      "PSC               73\n",
      "endothelial       16\n",
      "epsilon            7\n",
      "mast               7\n",
      "MHC class II       5\n",
      "Name: Celltype, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "query_adata = sc.read('demo_test.h5ad')\n",
    "query_adata = query_adata[:,ref_adata.var_names]\n",
    "print(query_adata)\n",
    "print(query_adata.obs.Celltype.value_counts())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Training:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cuda:0\n",
      "Mask loaded!\n",
      "Model builded!\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[train epoch 0] loss: 1.876, acc: 0.296: 100%|██████████| 5096/5096 [01:39<00:00, 51.10it/s]\n",
      "[valid epoch 0] loss: 0.305, acc: 0.967: 100%|██████████| 5096/5096 [00:34<00:00, 149.26it/s]\n",
      "[train epoch 1] loss: 0.168, acc: 0.975: 100%|██████████| 5096/5096 [01:40<00:00, 50.88it/s]\n",
      "[valid epoch 1] loss: 0.042, acc: 0.993: 100%|██████████| 5096/5096 [00:33<00:00, 152.25it/s]\n",
      "[train epoch 2] loss: 0.059, acc: 0.992: 100%|██████████| 5096/5096 [01:39<00:00, 51.08it/s]\n",
      "[valid epoch 2] loss: 0.024, acc: 0.996: 100%|██████████| 5096/5096 [00:33<00:00, 150.93it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training finished!\n",
      "label_dictionary.csv\n",
      "mask.npy\n",
      "model-0.pth\n",
      "model-1.pth\n",
      "model-2.pth\n",
      "pathway.csv\n"
     ]
    }
   ],
   "source": [
    "TOSICA.train(ref_adata, gmt_path='human_gobp', label_name='Celltype',epochs=3,project='hGOBP_demo')\n",
    "!ls ./hGOBP_demo"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cuda:0\n",
      "0\n",
      "4218\n"
     ]
    }
   ],
   "source": [
    "model_weight_path = './hGOBP_demo/model-0.pth'\n",
    "new_adata = TOSICA.pre(query_adata, model_weight_path = model_weight_path,project='hGOBP_demo')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "AnnData object with n_obs × n_vars = 4218 × 299\n",
       "    obs: 'Prediction', 'Probability', 'Celltype'\n",
       "    var: 'pathway_index'"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_adata.write('demo_attn.h5ad')\n",
    "new_adata"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_adata.raw = new_adata\n",
    "sc.pp.normalize_total(new_adata, target_sum=1e4)\n",
    "sc.pp.log1p(new_adata)\n",
    "sc.pp.scale(new_adata, max_value=10)\n",
    "sc.tl.pca(new_adata, svd_solver='arpack')\n",
    "sc.pp.neighbors(new_adata, n_neighbors=10, n_pcs=40)\n",
    "sc.tl.umap(new_adata)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "col = np.array([\n",
    "\"#98DF8A\",\"#E41A1C\" ,\"#377EB8\", \"#4DAF4A\" ,\"#984EA3\" ,\"#FF7F00\" ,\"#FFFF33\" ,\"#A65628\" ,\"#F781BF\" ,\"#999999\",\"#1F77B4\",\"#FF7F0E\",\"#279E68\",\"#FF9896\"\n",
    "]).astype('<U7')\n",
    "\n",
    "celltype = (\"alpha\",\"beta\",\"ductal\",\"acinar\",\"delta\",\"PP\",\"PSC\",\"endothelial\",\"epsilon\",\"mast\",\"macrophage\",\"schwann\",'t_cell')\n",
    "new_adata.obs['Prediction'] = new_adata.obs['Prediction'].astype('category')\n",
    "new_adata.obs['Prediction'] = new_adata.obs['Prediction'].cat.reorder_categories(list(celltype))\n",
    "new_adata.uns['Prediction_colors'] = col[1:]\n",
    "\n",
    "celltype = (\"MHC class II\",\"alpha\",\"beta\",\"ductal\",\"acinar\",\"delta\",\"PP\",\"PSC\",\"endothelial\",\"epsilon\",\"mast\")\n",
    "new_adata.obs['Celltype'] = new_adata.obs['Celltype'].astype('category')\n",
    "new_adata.obs['Celltype'] = new_adata.obs['Celltype'].cat.reorder_categories(list(celltype))\n",
    "new_adata.uns['Celltype_colors'] = col[:11]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1048.03x345.6 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sc.pl.umap(new_adata, color=['Celltype', 'Prediction'],legend_loc='on data')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Training on pre-weights of 'human_gobp' mask:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "!cp ./hGOBP_demo/model-0.pth ./pre_weights.pth"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cuda:0\n",
      "Mask loaded!\n",
      "<All keys matched successfully>\n",
      "Model builded!\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[train epoch 0] loss: 0.153, acc: 0.974: 100%|██████████| 5096/5096 [01:32<00:00, 55.28it/s]\n",
      "[valid epoch 0] loss: 0.040, acc: 0.992: 100%|██████████| 5096/5096 [00:31<00:00, 162.61it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training finished!\n"
     ]
    }
   ],
   "source": [
    "TOSICA.train(ref_adata, gmt_path='human_gobp', label_name='Celltype',pre_weights='pre_weights.pth',epochs=1)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.8.12 ('sc': conda)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.13"
  },
  "orig_nbformat": 4,
  "vscode": {
   "interpreter": {
    "hash": "36c23b859f2ca042e6d57f6e417a971df6eac071bc0bb6cce8f804977abf361f"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
